8000 MAINT: avoid nested dispatch in numpy.core.shape_base by shoyer · Pull Request #13634 · numpy/numpy · GitHub
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MAINT: avoid nested dispatch in numpy.core.shape_base #13634

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Merged
merged 3 commits into from
Jun 12, 2019
Merged

MAINT: avoid nested dispatch in numpy.core.shape_base #13634

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dff17a4
MAINT: avoid nested dispatch in numpy.core.shape_base
shoyer May 26, 2019
c0aae6b
MAINT: only remove internal use in np.block
shoyer Jun 1, 2019
48f8640
MAINT: fixup comment on np.block optimization
shoyer Jun 2, 2019
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21 changes: 14 additions & 7 deletions numpy/core/shape_base.py
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Original file line number Diff line number Diff line change
Expand Up @@ -123,7 +123,7 @@ def atleast_2d(*arys):
if ary.ndim == 0:
result = ary.reshape(1, 1)
elif ary.ndim == 1:
result = ary[newaxis,:]
result = ary[newaxis, :]
else:
result = ary
res.append(result)
Expand Down Expand Up @@ -193,9 +193,9 @@ def atleast_3d(*arys):
if ary.ndim == 0:
result = ary.reshape(1, 1, 1)
elif ary.ndim == 1:
result = ary[newaxis,:, newaxis]
result = ary[newaxis, :, newaxis]
elif ary.ndim == 2:
result = ary[:,:, newaxis]
result = ary[:, :, newaxis]
else:
result = ary
res.append(result)
Expand Down Expand Up @@ -416,6 +416,13 @@ def stack(arrays, axis=0, out=None):
return _nx.concatenate(expanded_arrays, axis=axis, out=out)


# Internal functions to elimainate the overhead of repeated dispatch inside
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spelling of eliminate. Also maybe write "repeated dispatch in one of the two possible paths inside np.block"

# np.block. Use getattr to protect against __array_function__ being disabled.
_size = getattr(_nx.size, '__wrapped__', _nx.size)
_ndim = getattr(_nx.ndim, '__wrapped__', _nx.ndim)
_concatenate = getattr(_nx.concatenate, '__wrapped__', _nx.concatenate)


def _block_format_index(index):
"""
Convert a list of indices ``[0, 1, 2]`` into ``"arrays[0][1][2]"``.
Expand Down Expand Up @@ -496,8 +503,8 @@ def _block_check_depths_match(arrays, parent_index=[]):
return parent_index + [None], 0, 0
else:
# We've 'bottomed out' - arrays is either a scalar or an array
size = _nx.size(arrays)
return parent_index, _nx.ndim(arrays), size
size = _size(arrays)
return parent_index, _ndim(arrays), size


def _atleast_nd(a, ndim):
Expand Down Expand Up @@ -640,7 +647,7 @@ def _block(arrays, max_depth, result_ndim, depth=0):
if depth < max_depth:
arrs = [_block(arr, max_depth, result_ndim, depth+1)
for arr in arrays]
return _nx.concatenate(arrs, axis=-(max_depth-depth))
return _concatenate(arrs, axis=-(max_depth-depth))
else:
# We've 'bottomed out' - arrays is either a scalar or an array
# type(arrays) is not list
Expand Down Expand Up @@ -858,7 +865,7 @@ def _block_slicing(arrays, list_ndim, result_ndim):

# Test preferring F only in the case that all input arrays are F
F_order = all(arr.flags['F_CONTIGUOUS'] for arr in arrays)
C_order = all(arr.flags['C_CONTIGUOUS'] for arr in arrays)
C_order = all(arr.flags['C_CONTIGUOUS'] for arr in arrays)
order = 'F' if F_order and not C_order else 'C'
result = _nx.empty(shape=shape, dtype=dtype, order=order)
# Note: In a c implementation, the function
Expand Down
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